Detection and tracking of moving cloud services from video using saliency map model. (19th May 2022)

• Record Type: Journal Article
• Authors: Kamble, Shailesh; Saini, Dilip Kumar J.; Kumar, Vinay; Gautam, Arun Kumar; Verma, Shikha; Tiwari, Ashish; Goyal, Dinesh
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 25:Number 4(2022)
• Page Start: 1083
• Page End: 1092
• Publication Date: 2022-05-19
• Subjects: Primary 93A30 -- Secondary 49K15; Saliency map -- Extended kalman filter -- Object detection -- Object tracking; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2022.2072436
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency map model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.